Collaborative Research: OAC Core: Simulation-driven runtime resource management for distributed workflow applications

在健康、气候建模、粒子物理学、地震学等领域的许多科学突破， 只能通过对大量数据进行复杂处理来实现。 这种处理由软件系统自动进行，这些软件系统使用网络基础设施提供的计算、存储和网络硬件。 除了自动化之外，这些系统的一个关键目标是通过成本和能源使用来衡量资源的有效利用，同时使处理尽可能快或按需进行。为此，这些系统必须决定哪些资源应该用于做什么以及何时做。 许多这样的系统在今天的生产中使用，并做出这样的决定。然而，做出好的决策，更不用说最好的决策，仍然是一个开放的研究挑战。理论研究已经提出了难以付诸实践的解决方案，而实际解决方案已知不会做出好的决策，或者至少不会始终如一地做出好的决策。 然而，理论和实践都遵循相同的基本哲学：通过推理关于需要计算的内容和可用硬件资源的已知信息来做出决策。这种哲学已经显示出它的局限性，所以这个项目采用了一种完全不同的方法。 其关键思想是重复执行快速，计算成本低的应用程序执行模拟，以评估大量的潜在资源管理决策，并自动选择最理想的。这种方法的好处将在用于支持对社会发展和可持续性至关重要的科学应用的几个软件系统中得到证明。软件系统用于在先进的网络基础设施上运行科学应用。 这些系统自动化应用程序执行，并沿着几个轴（包括选择和供应（虚拟化）硬件、挑选应用程序配置选项以及在时间和空间上调度应用程序活动）进行沿着资源管理决策。他们的目标是优化应用程序性能和一组资源使用效率指标，包括货币和能源成本。因此，资源管理决策空间是巨大的，做出好的决策是一个严峻的挑战，这是理论家和实践者无数努力的主题。 然而，挑战远未解决：理论家提出的解决方案很少被实践者使用，相反，实践者实施的解决方案可能是高度次优的，因为他们没有得到理论的指导。本项目通过消除制定有效的资源管理战略的需要来解决这种脱节。 其核心思想是使用在线模拟在运行时快速搜索资源管理决策空间。在整个执行过程中执行应用程序执行的大量快速模拟，以便评估许多潜在的资源管理选项并自动选择所需的选项。 因此，这种方法将整个问题从复杂的资源管理算法的设计转移到许多资源管理决策的枚举上。网络基础设施系统中资源管理实践的转变不仅使资源管理问题变得易于处理，而且还解锁了以前遥不可及的资源管理决策。 这一转变的好处将被证明为一个关键类的生产系统和应用程序，特别是工作流管理系统和科学应用程序，他们支持。这一奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

Many scientific breakthroughs in domains such as health, climate modeling, particle physics, seismology, etc., can only be achieved by performing complex processing of vast amounts of data. This processing is automated by software systems that use the compute, storage, and network hardware provided by the cyberinfrastructure. In addition to automation, a key objective of these systems is the efficient use of the resources as measured by cost and energy usage, while making the processing as fast as possible or as needed. To this end, these systems must make decisions regarding which resources should be used to do what and when. Many such systems are used in production today and make such decisions. Yet making good, let alone best, decisions is still an open research challenge. Theoretical research has proposed solutions that are difficult to put into practice, and practical solutions are known to not make good decisions, or at least not consistently so. However, both theory and practice follow the same basic philosophy: make decisions by reasoning about known information on what needs to be computed and on what hardware resources are available. This philosophy has shown its limits, so this project adopts a radically different approach. The key idea is to repeatedly execute fast, computationally inexpensive simulations of the application execution in order to evaluate large sets of potential resource management decisions and automatically select the most desirable ones. The benefits of this approach will be demonstrated for several software systems used to support scientific applications that are critical for the development and sustainability of society.Software systems are used to run scientific applications on advanced cyberinfrastructure. These systems automate application execution, and make resource management decision along several axes including selecting and provisioning (virtualized) hardware, picking application configuration options, and scheduling application activities in time and space. Their objective is to optimize both application performance and also a set of resource usage efficiency metrics that include monetary and energy costs. Consequently, the resource management decision space is enormous, and making good decisions is a steep challenge that has been the subject of countless efforts, both from theoreticians and practitioners. However, the challenge is far from being solved: theoreticians produce solutions that are rarely used by practitioners, and conversely practitioners implement solutions that may be highly sub-optimal because they not informed by theory. This project resolves this disconnect by obviating the need for developing effective resource management strategies. The key idea is to use online simulations to search the resource management decision space rapidly at runtime. Large numbers of fast simulations of the application's execution are executed throughout that very execution, so as to evaluate many potential resource management options and automatically select desirable ones. This approach thus shifts the overall problem from the design of complex resource management algorithms to the enumeration of many resource management decisions. The transformation of resource management practice in cyberinfrastructure systems not only renders the resource management problem tractable but also unlocks previously out-of-reach resource management decisions. The benefits of this transformation will be demonstrated for a critical class of production systems and applications, specifically Workflow Management Systems and the scientific applications they support.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.